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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 291–300 of 796 papers

TitleStatusHype
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Constructing High Frequency Economic Indicators by Imputation—0
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
A Denoising View of Matrix Completion—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Conservative Stochastic Optimization with Expectation Constraints—0
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